Asphaltenes are a sub-component of crude oil that form sticky aggregates when a shift in the native solubility matrix is caused by a change in pressure, temperature, or composition of the oil. The thermodynamics of asphaltene stability, the mechanisms of agglomeration, and the models for deposition are the focus of intense and active areas of research.
Unintended precipitation and deposition of asphaltene from reservoir fluids can happen during production, transportation, and processing operations. These deposits can lead to reservoir impairment, plugging near the wellbore, restriction in flowlines, as well as equipment failures and processing challenges for surface facilities. As such, flow assurance that accounts for possible precipitation and deposition of asphaltene from reservoir fluid relies heavily on frequent and accurate measurements, particularly when characterizing the asphaltene phase behavior within a crude sample.
Asphaltenes of a crude oil are conventionally defined as being poorly soluble in n-alkanes (e.g., n-heptane) and highly soluble in aromatic solvents (e.g., toluene). With this broad definition, the asphaltenes are a fraction of a crude oil sample that can vary from one crude oil sample to another. The complex mixture of asphaltene molecules can be characterized with a distribution of varying solubility parameters; ranging from the least soluble (less stable asphaltenes) to the most soluble (more stable asphaltenes). Gradual titration of stock tank crude oil or gradual depressurization of live crude oil is most often used to measure the solubility profile of the asphaltene fraction. The proportional amount of asphaltene precipitation can be measured by controllably sweeping the level of perturbation to the native crude oil. This profile can then be related to flow assurance control schemes and models. For example, as the amount of n-alkane (or titrant) is varied, only a fraction of the total amount of asphaltene precipitates. The remainder of asphaltenes stay in solution due to partial solubility. An asphaltene yield curve can be created by scanning a range of titrant-oil fractions, which is a plot relating the amount of precipitated asphaltenes as a function of titrant concentration. The data contained in the yield curve is related to asphaltene solubility or the phase separation of asphaltenes. Key parameters, like the asphaltenes precipitation onset point, can be extracted from such titration curves.
There are a number of techniques used to detect and measure the extent of asphaltene precipitation, including: visual observation, absorption and fluorescence spectroscopy, light scattering, refractive index-based methods, conductivity, acoustic resonance and filtration methods, viscosity, and the conventional gravimetric approach.
Currently, the asphaltene onset condition (pressure, temperature, and composition) in crude oil is determined by systematic depressurization (at constant temperature) of a sample of the crude oil in a PVT cell in the laboratory. In the PVT cell, precipitation of asphaltene is detected based on visual observation and light scattering. Another approach for detecting the onset of asphaltene precipitation and yield is to measure the crude oil refractive index during temperature, pressure, or composition perturbations. Buckley, J. S., Predicting the Onset of Asphaltene Precipitation from Refractive Index Measurements. Energy & Fuels, 1999, 13(2): p. 328-332 presents a graph of the measured refractive index (RI) for a mixture of n-heptane and oil. The mixture RI gradually decreases as n-heptane is added to a sample crude oil. When the asphaltene onset condition is reached, the mixture RI sharply decreases indicated by a difference in slopes. Sudden changes in RI indicate a phase transition. Surface plasmon resonance (SPR) spectra can also be used to determine the refractive index of the sample, which in turn may be used to measure solubility parameters of hydrocarbon fluids.
Furthermore, there are relatively few methods to monitor and characterize asphaltene deposition in real-time. Most often, a deposition experiment monitors the time-wise pressure change across a capillary tube or porous media while flowing crude oil through the system under specific conditions. The relative pressure change is determined using the Hagen-Poiseuille equation, assuming uniform deposition thickness on the wall surface along the entire flow-line length. When relating deposit thickness to pressure drop, it is further assumed that flow rate and viscosity remain constant. To achieve the required sensitivity, multiple pressure transducers with overlapping dynamic ranges are coupled to the entry port of a long stainless steel tube. It is necessary to have long tube lengths of 16-32 m with small cross-sections of 0.5 mm diameter and slow flowrates of approximately 5 mL/hr as described in Wang et al., “Asphaltene Deposition on Metallic Surfaces,” Journal of Dispersion Science and Technology, Vol. 25(3), 2004, pgs. 287-298. Creating measurable deposits, 1-100 μm, often takes 50-100 hours or 2-4 days. Variations in deposition thickness, e.g. constricted regions or plugs, are not easily measured and detrimentally impact the apparent deposition thickness. Gradation can be accomplished with multiple sensor ports incorporated into the flow-line, but this creates added dead-volume and geometry changes at each pressure transducer junction. With flowline deposition experiments, one can also perform post-characterization of deposits in a batch-like manner. At the conclusion of the run, the surfaces of a Taylor-Couette device/chamber, or segments of the flowline, are rinsed with a solvent to capture the deposit, which is then concentrated and measured gravimetrically. These methods are excellent for detailed characterization of the deposit, but do not provide online feedback as the deposit is formed. Flowline deposition experiments therefore lack the sensitivity to observe initial adsorbed asphaltene layers and require significant runtimes.
Real-time observations of deposit formation have been made using a Quartz Crystal Microbalance with Dissipation (QCM-D) as described in Abudu et al., “Adsorption of Crude Oil on Surfaces Using Quartz Crystal Microbalance with Dissipation (QCM-D) under Flow Conditions,” Energy and Fuels, Vol. 23(3), 2009, pgs. 1237-1248. The QCM-D measurements can be performed during titration experiments and achieve high mass sensitivity based on the electromechanical response of an oscillating piezoelectric sensor. Relating frequency shift and mass change in a vacuum or a gas environment can be accomplished with the Sauerbrey equation. QCM in a liquid environment like when immersed in crude oil is more complicated. The frequency shift depends on the chamber pressure, deposit mass loading (asphaltenes-viscoelastic films), liquid loading, liquid trapping, and surface roughness. Decoupling the deposited asphaltene mass from the other system attributes that impact the frequency shift requires tuned models. Often, correction factors and prior knowledge of the crude oil density and viscosity are required. Tavakkoli et al. performed a two-part detailed study of the factors influencing QCM-D measurements when coupled with titration experiments. See Tavakkoli et al., “Asphaltene Deposition in Different Depositing Environments: Part 1. Model Oil”, Energy & Fuels, Vol. 28(3), 2014, pgs. 1617-1628; and Tavakkoli et al., “Asphaltene Deposition in Different Depositing Environments: Part 2. Real Oil,” Energy & Fuels, Vol. 28(6), 2014, pgs. 3594-3603. They also evaluated deposition tendency using crystal surfaces coated with a variety of materials, including: gold, carbon steel, and iron oxide. Their work highlights the key advantages of QCM-D, namely: the sensitivity to detect nanograms of adsorbed mass, the ability to select relevant surface coatings, and fast measurement times (˜hours). However, online QCM sensing of the deposit formation during flow conditions requires real-time thin-film density information to decouple entrapped fluid mass from asphaltene deposit mass. To solve a similar problem, Reimhult et al. combined QCM-D with surface plasmon resonance (SPR) to simultaneously measure the mass reported by both methods for an aqueous biomolecular system as described in Reimhult et al., “Simultaneous surface plasmon resonance and quartz crystal microbalance with dissipation monitoring measurements of biomolecular adsorption events involving structural transformations and variations in coupled water,” Analytical Chemistry, Vol. 76(24), 2004, pgs. 7211-7220. QCM-D data was used to determine the total adsorbed thin-film mass (acoustically derived), while SPR data was used to determine the adsorbed biomolecule mass (optically derived) via refractive index of the thin-film decoupled from dynamically bound water. Reimhult et al. employed an iterative calculation process that incorporated physical models of the QCM-D/SPR approaches and determined accurate thin-film properties: thickness, density, total mass, water mass, and biomolecular mass. Lastly, realizing QCM-D devices at reservoir pressures that range from 5-30 kpsi will be challenging as most demonstrations with crude oil fluids are performed near atmospheric pressure. Studies show that it is feasible to build QCM systems rated to 3 kpsi, but thus far, the technique is generally limited to 5-6 kpsi.